Project Summary Alzheimers disease (AD) is a highly heritable, progressive neurodegenerative disorder. However, the genetic basis of AD is complex and the molecular basis of disease risk remains poorly understood. As a result, AD is essentially untreatable. Available drugs for AD are only marginally effective. AD risk increases exponentially with age with a prevalence of 3 5% by 65 69 years increasing to 30 40% by 85 89 years. A speci?c promise of the genome-wide association study (GWAS) era was that genetic associations would translate into improved disease prediction, prevention, and therapeutic development, but we have not seen this promise ful?lled. While GWAS have rapidly and reproducibly identi?ed genetic loci associated with complex diseases such as AD, critical limitations restrict their translational impact. The vast majority of identi?ed disease risk loci are not within protein- coding genes, but rather lie within cis-regulatory elements (CREs), many of which are tissue speci?c. This suggests that most complex disease risk variants modify the function of a CRE, which impacts gene expression, which, in turn, affect disease risk. This has impeded our understanding of complex disease mechanisms because it is typically dif?cult to identify the relevant genes and pathways from non-coding GWAS associations alone. Moreover, follow up experimental characterization of candidate regions has proven expensive and laborious. In this proposal, we will address these critical limitations by taking a combined statistical and experimental approach to identify the precise genetic variants and genes that affect AD risk. In Aim 1, we will identify AD-associated CREs and their target genes across ten brain regions. In Aim 2, we will develop statistical methods to integrate gene expression and disease association data to identify the genes whose expression levels causally affect AD risk. Finally, in Aim 3, we propose to validate the predictions made in Aims 1 and 2 with a combination of high throughput reporter assays and targeted genome engineering. Throughout this proposal, we will develop, evaluate, and make public new analytic tools that take advantage of many-core computing environments, and will make publicly available all of the genetic and epigenetic data generated from the brain samples.